Document Text Classification

The CIFAR-100 dataset (Canadian Institute for Advanced Research, 100 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. There are 600 images per class. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs). There are 500 training images and 100 testing images per class. The criteria for deciding whether an image belongs to a class were as follows: * The class name should be high on the list of likely answers to the question “What is in this picture?” * The image should be photo-realistic. Labelers were instructed to reject line drawings. * The image should contain only one prominent instance of the object to which the class refers. * The object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler. Source: https://www.cs.toronto.edu/~kriz/cifar.html Image Source: https://www.cs.toronto.edu/~kriz/cifar.html

The Caltech-UCSD Birds-200-2011 (CUB-200-2011) dataset is the most widely-used dataset for fine-grained visual categorization task. It contains 11,788 images of 200 subcategories belonging to birds, 5,994 for training and 5,794 for testing. Each image has detailed annotations: 1 subcategory label, 15 part locations, 312 binary attributes and 1 bounding box. The textual information comes from Reed et al.. They expand the CUB-200-2011 dataset by collecting fine-grained natural language descriptions. Ten single-sentence descriptions are collected for each image. The natural language descriptions are collected through the Amazon Mechanical Turk (AMT) platform, and are required at least 10 words, without any information of subcategories and actions. Source: Fine-grained Visual-textual Representation Learning Image Source: http://www.vision.caltech.edu/visipedia/CUB-200-2011.html

The Medical Information Mart for Intensive Care III (MIMIC-III) dataset is a large, de-identified and publicly-available collection of medical records. Each record in the dataset includes ICD-9 codes, which identify diagnoses and procedures performed. Each code is partitioned into sub-codes, which often include specific circumstantial details. The dataset consists of 112,000 clinical reports records (average length 709.3 tokens) and 1,159 top-level ICD-9 codes. Each report is assigned to 7.6 codes, on average. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework. Source: MIT Laboratory for Computational Biology

The Food-101 dataset consists of 101 food categories with 750 training and 250 test images per category, making a total of 101k images. The labels for the test images have been manually cleaned, while the training set contains some noise. Source: Combining Weakly and Webly Supervised Learning for Classifying Food Images Image Source: https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/

VoxCeleb1 is an audio dataset containing over 100,000 utterances for 1,251 celebrities, extracted from videos uploaded to YouTube.

Clothing1M contains 1M clothing images in 14 classes. It is a dataset with noisy labels, since the data is collected from several online shopping websites and include many mislabelled samples. This dataset also contains 50k, 14k, and 10k images with clean labels for training, validation, and testing, respectively. Source: Label-Noise Robust Generative Adversarial Networks Image Source: https://openaccess.thecvf.com/content_cvpr_2015/papers/Xiao_Learning_From_Massive_2015_CVPR_paper.pdf

The WebVision dataset is designed to facilitate the research on learning visual representation from noisy web data. It is a large scale web images dataset that contains more than 2.4 million of images crawled from the Flickr website and Google Images search. The same 1,000 concepts as the ILSVRC 2012 dataset are used for querying images, such that a bunch of existing approaches can be directly investigated and compared to the models trained from the ILSVRC 2012 dataset, and also makes it possible to study the dataset bias issue in the large scale scenario. The textual information accompanied with those images (e.g., caption, user tags, or description) are also provided as additional meta information. A validation set contains 50,000 images (50 images per category) is provided to facilitate the algorithmic development.

This work presents two new benchmark datasets (CIFAR-10N, CIFAR-100N), equipping the training dataset of CIFAR-10 and CIFAR-100 with human-annotated real-world noisy labels that we collect from Amazon Mechanical Turk.

This work presents two new benchmark datasets (CIFAR-10N, CIFAR-100N), equipping the training dataset of CIFAR-10 and CIFAR-100 with human-annotated real-world noisy labels that we collect from Amazon Mechanical Turk.

Chaoyang dataset contains 1111 normal, 842 serrated, 1404 adenocarcinoma, 664 adenoma, and 705 normal, 321 serrated, 840 adenocarcinoma, 273 adenoma samples for training and testing, respectively. This noisy dataset is constructed in the real scenario. - Details: Colon slides from Chaoyang hospital, the patch size is 512 × 512. We invited 3 professional pathologists to label the patches, respectively. We took the parts of labeled patches with consensus results from 3 pathologists as the testing set. Others we used as the training set. For the samples with inconsistent labeling opinions of the three doctors in the training set (this part accounts for about 40%), we randomly selected the opinions from one of the three doctors. - The original WSIs are scanned at X20 objective magnification.

10 classes with 50, 000 training and 5, 000 testing images. Please note that, in ANIMAL10N, noisy labels were injected naturally by human mistakes, where its noise rate was estimated at 8%.

Approx. 300,000 images of galaxies labelled by shape. Labels are from www.galaxyzoo.org volunteers, and are noisy. Images are from the DECaLS telescope survey. Also includes predictions from an ensemble of EfficientNets, each using MC Dropout and a novel probabilistic loss function.

Kompetencer (en: competences) is a Danish job posting dataset annotated for nested spans of competences.

Part of the Controlled Noisy Web Labels Dataset.

Part of the Controlled Noisy Web Labels Dataset.

Part of the Controlled Noisy Web Labels Dataset.

The Tobacco-3482 dataset consists of document images belonging to 10 classes such as letter, form, email, resume, memo, etc. The dataset has 3482 images.

TwinViews-13k is a dataset of 13,855 pairs of left-leaning and right-leaning political statements, each pair matched by topic. It was created to study political bias in reward and language models, with a focus on understanding the interaction between model alignment to truthfulness and the emergence of political bias. The dataset was generated using GPT-3.5 Turbo, with extensive auditing to ensure ideological balance and topical relevance. This dataset can be used for various tasks related to political bias, natural language processing, and model alignment, particularly in studies examining how political orientation impacts model outputs.

COCO-N Medium introduces a stochastic benchmark that simulates common real-world scenarios with noticeable label inaccuracies in the COCO dataset. This benchmark combines class and spatial noises to create a challenging yet realistic evaluation framework for instance segmentation models. It mimics datasets manually annotated by crowd workers, where a moderate level of label noise is expected. By incorporating both class and spatial inaccuracies, COCO-N Medium allows researchers to assess their models' basic robustness to label noise, providing insights into performance in typical real-world applications where perfect annotations are rare. This medium-level benchmark serves as a crucial middle ground, offering a more rigorous test than minimally noisy datasets while remaining within the bounds of commonly encountered data quality issues. COCO-N Medium enables a nuanced evaluation of model performance under realistic conditions, helping identify areas for improvement in handling noisy labels and guiding the development of more robust instance segmentation algorithms.

The COCO-WAN benchmark is designed to assess the impact of weakly annotations (combined with auto-annotation tools) noise on instance segmentation models. This benchmark is built upon the COCO dataset and incorporates noise generated through weak annotations, simulating real-world scenarios where annotations might be imperfect due to semi-automated tools. It includes various levels of noise to challenge the robustness and generalization capabilities of segmentation models. Accurately labeling instance segmentation datasets is a complex and error-prone task, often leading to noisy labels. The COCO-WAN benchmark aims to provide a realistic testing ground for models to handle such noisy annotations. By utilizing foundation models and weak annotations, COCO-WAN simulates semi-automated annotation tools, helping researchers understand how well their models can perform under less-than-ideal labeling conditions. This benchmark includes multiple noise levels (easy, medium, and hard) to reflect varying degrees of annotation imperfections. Potential Use Cases of the Dataset: Model Robustness Testing: Researchers can use COCO-WAN to evaluate how different instance segmentation models cope with noisy annotations, allowing for the development of more resilient algorithms. Annotation Tool Improvement: By analyzing model performance on COCO-WAN, developers of annotation tools can identify common pitfalls and work on reducing noise in their outputs. Semi-Automated Annotation Systems: The benchmark provides insights into how models trained with semi-automated annotations perform, guiding improvements in such systems for better accuracy and efficiency in labeling tasks. The COCO-WAN benchmark offers a crucial resource for advancing the field of instance segmentation by highlighting the challenges posed by noisy labels and fostering the creation of more robust and reliable models. Model Robustness Testing: Researchers can use COCO-WAN to evaluate how different instance segmentation models cope with spatial, real noisy annotations, allowing for the development of more resilient algorithms. Semi-Automated Annotation Systems: The benchmark provides insights into how models trained with semi-automated annotations perform, guiding improvements in such systems for better accuracy and efficiency in labeling tasks. The COCO-WAN benchmark offers a crucial resource for advancing the field of instance segmentation by highlighting the challenges posed by noisy labels and fostering the creation of more robust and reliable models.

MatriVasha the largest dataset of handwritten Bangla compound characters for research on handwritten Bangla compound character recognition. The proposed dataset contains 120 different types of compound characters that consist of 306,464‬ images written where 152,950 male and 153,514 female handwritten Bangla compound characters. This dataset can be used for other issues such as gender, age, district base handwriting research because the sample was collected that included district authenticity, age group, and an equal number of men and women.

The WOS Hierarchical Text Classification are three dataset variants created from Web of Science (WOS) title and abstract data categorised into a hierarchical, multi-label class structure. The aim of the sampling and filtering methodology used was to create well-balanced class distributions (at chosen hierarchical levels). Furthermore, the WOS_JTF variant was also created with the aim to only contain publication data such that their class assignments results is classes instances that semantically more similar. The three dataset variants have the following properties: 1. WOS_JT comprises 43,366 total samples (train=30356, dev=6505, test=6505) and only uses the journal-based classifications as labels. 2. WOS_CT comprises 65,200 total samples (train=45640, dev=9780, test=9780) and only uses citation-based classifications as labels. 3. WOS_JTF comprises 42,926 total samples (train=30048, dev=6439, test=6439) and uses a filtered set of papers based on journal and citation classification. The dataset is available at: https://huggingface.co/datasets/marcelsun/wos_hierarchical_multi_label_text_classification Dataset details: *.json: - concatenated title and abstract mapped to a list each associated class label. depth2label.pt: dictionary where: - key = depth of classification hierarchy. - value = list of classes associated with depth. path_list.pt: - list of tuples for every edge between classes in the hierarchical classification. This specifies the acyclic graph. slot.pt: dictionary where: - key = label_id of parent class - value = label_ids of children classes value2slot.pt: dictionary where: - key = label_id - value = label_id of parent class value_dict.pt: dictionary where: - key = label_id - value = string representation of class.